Using instrumental variables to address bias from unobserved confounders


2 May 2019 - Randomised clinical trials are considered the most reliable source of evidence for the effects of medical interventions, but non-experimental studies are often used to assess the effectiveness of treatments as they are used in actual clinical practice. 

In non-experimental studies, treatment groups may differ by important patient characteristics, such as disease severity, frailty, cognitive function, vulnerability to adverse effects, and ability to pay. 

While statistical adjustment can account for imbalances in observed characteristics between groups, observed imbalances are concerning because they suggest that unobserved differences may also exist. Unobserved patient characteristics that influence both treatment and the outcomes result in “unobserved confounding,” a bias that cannot be removed using standard statistical adjustment.

Read JAMA Guide to Statistics and Methods article

Michael Wonder

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Michael Wonder

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Medicine , US , Bias , Statistics